We demonstrated several techniques for encouraging the practical use of neural networks on fluid flow problems.
We first focused on visualization inside neural networks from the perspective of interpretability, considering two techniques, i.e., layer visualization and use of Grad-CAM.
The great ability of them could be appreciated from observing the grounds of the estimation.
Especially, the Grad-CAM offered us a clear insight of the crucial region for the estimation.
The use of various data augmentation techniques for training dataset, i.e., data flipping, noise addition and transfer learning, were also considered with canonical fluid flow regressions.
Among the covered techniques, we also found that transfer learning through local data can be a great candidate for improving the estimation ability drastically and stably in most of the cases.
Lastly, we investigated the generalizability of the neural networks for unlearned data through super-resolution analysis.
The trained network was able to catch the rough structure of the flow field even with the flows that are not included in the training data.
In particular, the lower error was reported on the flows which has similar characteristics to the training data according to a singular value spectrum.
Regarding the variation in data, we investigated the capability of the machine-learned model for unseen data by considering the temporal evolution of a flow around a bluff body with various different shapes in our previous paper [1]}.
Moreover, the dependence of the model performance on Reynolds number is also investigated in [2]}.
These observations also tell us that when the test situation is not too far from the training situation, and the training data are sufficiently given, a machine-learning model can hold the generalizability even for unseen data.
